4.6 Article

Quantifying the Early Immune Response and Adaptive Immune Response Kinetics in Mice Infected with Influenza A Virus

期刊

JOURNAL OF VIROLOGY
卷 84, 期 13, 页码 6687-6698

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AMER SOC MICROBIOLOGY
DOI: 10.1128/JVI.00266-10

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  1. National Institute of Allergy and Infectious Diseases [N01-AI-50020, R01 AI069351]
  2. U.S. Department of Energy [DE-AC52-679 06NA25396]

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Seasonal and pandemic influenza A virus (IAV) continues to be a public health threat. However, we lack a detailed and quantitative understanding of the immune response kinetics to IAV infection and which biological parameters most strongly influence infection outcomes. To address these issues, we use modeling approaches combined with experimental data to quantitatively investigate the innate and adaptive immune responses to primary IAV infection. Mathematical models were developed to describe the dynamic interactions between target (epithelial) cells, influenza virus, cytotoxic T lymphocytes (CTLs), and virus-specific IgG and IgM. IAV and immune kinetic parameters were estimated by fitting models to a large data set obtained from primary H3N2 IAV infection of 340 mice. Prior to a detectable virus-specific immune response (before day 5), the estimated half-life of infected epithelial cells is similar to 1.2 days, and the half-life of free infectious IAV is similar to 4 h. During the adaptive immune response (after day 5), the average half-life of infected epithelial cells is similar to 0.5 days, and the average half-life of free infectious virus is similar to 1.8 min. During the adaptive phase, model fitting confirms that CD8(+) CTLs are crucial for limiting infected cells, while virus-specific IgM regulates free IAV levels. This may imply that CD4 T cells and class-switched IgG antibodies are more relevant for generating IAV-specific memory and preventing future infection via a more rapid secondary immune response. Also, simulation studies were performed to understand the relative contributions of biological parameters to IAV clearance. This study provides a basis to better understand and predict influenza virus immunity.

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